System and method for determining art preferences of people

ABSTRACT

A system and method of the present invention present a web based music station. The system includes a website having at least two engine components such as a table of a playlist and a table of a content references. A system includes a controller with an algorithm incorporated therein. The algorithm is configured to retrieve various images and music pre-stored in the controller and presents these images and the music to the users. The system tries to reconstruct the “logical priority chains” of user perception as it relates to both the music and the images. The system then clusterizes picks of the music and the images and the users. The system then binds both music and images together using non-linear mapping to determine preference of each user.

RELATED APPLICATIONS

This is a non-provisional application that claims priority to aprovisional application Ser. No. 61/520,196 filed on Jun. 6, 2011 and aprovisional application Ser. No. 61/571,594 filed on Jun. 30, 2011 andincorporated herewith by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates to an Internet based systems and methodsfor information retrieval and, in particular, to a system and method fordetermining preferences of people.

BACKGROUND OF THE INVENTION

The broadcasting of radio by means of the Internet is a fairly recentdevelopment and becomes more and more popular among users. Typically, asa listener logs onto a particular web site, audio files, usually songs,are played. The selections of which audio files are to be played arecontrolled by the owner or operator of the web site. In the past, inconjunction with conventional radio a desirable procedure was to get thelisteners involved in conjunction with the broadcasting station.Generally, this type of procedure increased the number of listeners, andof course the greater number of listeners the more successful a radiostation. One way to get the listeners involved is to have the radiostation accept requests for particular songs or to hear certain audiofiles.

The art is replete with numerous prior art Internet based radio systemsand methods. With the ever-growing popularity of acquiring music, avariety of these prior art consumer devices such as a digital mediaplayer (DMP) or a digital audio player (DAP) are used to play and managedigital music files, wherein these consumer devices may be a singlefunctional device, a multifunctional device, such as a mobile phone, apersonal digital assistant (PDA), or a handheld computer. Since thesetypes of prior art consumer devices continually become more portable andversatile, our reliance on such devices for entertainment purposes hasgrown. In some instances, a user may create a playlist. The playlist mayinclude one or more songs selected by the user that may be played, forexample, in sequence or in random order. However, the process ofcreating a playlist can be time-consuming and burdensome.

There are numerous systems and methods in the prior art that allow theusers of these aforementioned prior art devices to download use aprincipal component analysis (PCA) in order to determine likeness ofcertain music type of the users. The PCA is a mathematical procedurethat uses an orthogonal transformation to convert a set of observationsof possibly correlated variables into a set of values of uncorrelatedvariables called principal components. The number of principalcomponents is less than or equal to the number of original variables.This transformation is defined in such a way that the first principalcomponent has as high a variance as possible (that is, accounts for asmuch of the variability in the data as possible), and each succeedingcomponent in turn has the highest variance possible under the constraintthat it be orthogonal to (uncorrelated with) the preceding components.Principal components are guaranteed to be independent only if the dataset is jointly normally distributed.

The PCA is mostly used as a tool in exploratory data analysis and formaking predictive models. The PCA can be done by eigenvaluedecomposition of a data covariance matrix or singular valuedecomposition of a data matrix, usually after mean centering the datafor each attribute. The results of the PCA are usually discussed interms of component scores (the transformed variable values correspondingto a particular case in the data) and loadings (the weight by which eachstandardized original variable should be multiplied to get the componentscore). The PCA is the simplest of the true eigenvector-basedmultivariate analyses. If a multivariate dataset is visualised as a setof coordinates in a high-dimensional data space (1 axis per variable),the PCA can supply the user with a lower-dimensional picture, a “shadow”of this object when viewed from its (in some sense) most informativeviewpoint. This is done by using only the first few principal componentsso that the dimensionality of the transformed data is reduced.

Numerous prior art references use the PCA approach in its systems andmethods. One of such prior art references in United States PatentApplication Publication No. 20090116684 to Andreasson (the Andreassonreference). The Andreasson reference teaches a system and method forgenerating a playlist of songs based on facial expression of a user. Themethod includes playing a first song on a device and capturing an imageof a user, performing facial expression recognition of the user based onthe image, and selecting a second song based on a facial expression ofthe user. The method taught by the Andreasson reference fails to solvethe aforementioned problems because the process of creating a playlistof songs based on this method will be time-consuming and burdensomesimply because different users have different personalities and notevery user will show facial expressions in response to the song playedor image presented to the user. Some users may still have facialexpressions that will not be captured by the system of the Andreassonreference. Another problem of the system taught by the Andreassonreference is inaccuracy of the facial expression determination becausesome users may present such facial expression that can be visible to thesystem as if the user is unhappy with the image presented to the userwherein, in fact, the user likes the image.

Another prior art reference, namely United States Patent ApplicationPublication No. 20080021851 to Alcade et al. (the Alcade reference)teaches system uses the PCA approach, wherein a series of complexartificial intelligence algorithms analyze a plurality of soniccharacteristics in a musical composition, and is then able to sort anycollection of digital music based on any combination of similarcharacteristics. The characteristics analyzed are those that produce thestrongest reaction in terms of human perception, such as melody, tempo,rhythm, and range, and how these characteristics change over time. Thisapproach enables the creation of “constellations” of music with similarcharacteristics, even from different genres and styles, enabling fastyet highly individualized music discovery. Further personalized musicdiscovery is enabled based on a “Music Taste Test”.

To provide users with music recommendations, the system employs a numberof analysis functions. A “Music Taste Test” (MI Mood module) functionlearns a user's music preferences via a series of binary choicequestions, and delivers lists and/or personalized song recommendationsto the user based on this information. Recommendations are prioritizedand listed in order of closest song match on a theoreticalmulti-dimensional grid. A “Soundalikes” function links songs havingsimilar musical/mathematical profiles enabling for music recommendation.A “Discovery” function that also links songs having similar mathematicalpatterns, but that allows for a wider recommendation than the“Soundalikes” function. The “Music Taste Test” function and“Soundalikes” function cooperate to establish ‘moods’ for each song,such as happy, sad, calm, and energetic.

To the extend effective and more advanced as compared with the systemand method of the Andreasson reference, the system of the Alcadereference presents numerous drawbacks. For example, not every user willbe willing to go through a plurality of questions in order to answerthem to determine the type of music that the user will like. Thisprocedure is time consuming and to some extent may not be practicable tothose users who may not understand English or not understand thequestion.

Another prior art reference such as U.S. Pat. No. 4,839,853 toDeerwester et al. (the Deerwester reference) teaches a method of latentsemantic analysis (the LSA), which is completely different from the PCAapproach. This method presents a technique in natural languageprocessing, in particular in vectorial semantics, wherein the methodanalyzes relationships between a set of documents and the terms theycontain by producing a set of concepts related to the documents andterms. As further taught by the Deerwester reference, the LSA assumesthat words that are close in meaning will occur close together in text.A matrix containing word counts per paragraph (rows are represented byunique words and columns are represented by each paragraph) isconstructed from a large piece of text and a mathematical techniquecalled singular value decomposition (the SVD) is used to reduce thenumber of columns while preserving the similarity structure among rows.Words are then compared by taking the cosine of any two rows. Valuesclose to 1 represent very similar words while values close to 0represent very dissimilar words.

To the extend effective, the LSA application as disclosed in theDeerwester reference fails to teach application that will allow todetermine preference of the users to certain type of music therebyclusterizing the users into groups in order to provide the users withmusic of their preference.

Therefore, an opportunity exists for an improved system and methodwhereby users will enjoy playlist of songs based on the user's choiceand preference will be presented to the users based on initialquestionnaire wherein the users will not select songs to create theplaylist thereby eliminating the need for creation of such playlist thatis time-consuming and burdensome.

SUMMARY OF THE INVENTION

A system and method of the present invention is used to determinepersonal preferences of users in music, movies, poetry, and any otherforms of art and clusterizing the users according to their preferences.Those skilled in the art will appreciate that the system and method ofthe present invention may be used to in other areas of research anddevelopment where there is a need to determine preferences in variousfields for the purposes of research, marketing, and the like. The usersaccess the system through personal communication devices, such as, amobile phone, a personal computer, and the like. The system includes auser interface component for receiving information from the personalcommunication devices. The interface component is operably communicatedwith an image controller, a song controller, and a coordinatingcontroller with all of the aforementioned components being cooperablewith one another and adaptable to receive and exchange informationbetween one and the other.

A central engine or a central processing unit (the CPU) is adaptable toreceive information from the coordinating controller. The CPU iscommunicated with a content delivery network or content distributionnetwork (CDN). As appreciated by those skilled in the art the CDN is asystem of computers containing copies of data placed at various nodes,such as the nodes of the present invention. There numerous data typescached in the CDN of the present invention. These data includes webobjects, downloadable objects (media files, software, documents),applications, live streaming media, and database queries withoutlimiting the scope of the present invention. The CPU includes at leastone sub component with a software presenting an algorithm.

The software presents an operable connection with the coordinatingcomponent and is configured to algorithmically calculate variousdistances between locations a first user and a second user and each of aplurality of the first test elements, such as graphical illustrations,pictures, videos, and the like, and a plurality of the second testelements such as for example, songs, various melodies, etc. The numberof users is unlimited and the first and second users as mentioned aboveare not intended to limit the scope of the present invention and arepresented for explanatory purposes. The software is configured toidentify and map location of each user in relation to the first andsecond test elements on a multidimensional surface and relationshipbetween the first and second user and the test elements selected by thefirst and second users to determine consecutive orders of the first testelements and the second test elements relative to the locations of eachof the first and second users. The software then identifying a firstcluster and a second cluster based on difference between values of theconsecutive orders.

Alluding to the above, the software is configured to algorithmicallycalculate various distances between a location of a third user and saidplurality of the first test elements and the second test elementspresented to and selected by the third user to determine a consecutiveorder of the first test elements and the second test elements relativeto the location of the third user thereby assigning the third usereither to the first cluster or the second cluster as the softwaredetermines a match between the values of one of the consecutive ordersof the first and second users and the value of the consecutive order ofthe third user.

In general the algorithm of the present invention picks various firsttest elements, i.e. images of good dispersion, good average user ratingfrom the initial set of images stored in the image controller, andpreviously viewed by many other users who pre-tested the system. Aftergetting of triplet marks, the system tries to reconstruct the “logicalpriority chains” of user perception. For example, if in a firstcombination of images or a first triplet, i.e. images 1, 2, 3, the userselects the image 1, and in a second combination of images or a secondtriplet 1, 2, 4, the user selects image 1, and then in a thirdcombination of images or a third triplet 2, 3, 4, the user selects image4, then the chain of the images selected by the user will be presented:1>4>(2 and 3). Such chains are being restored in the CPU for numerouscombinations of images.

Then the CPU will find the approximate coordinates of the user picks ina space of properties in order to clusterize picks and users. The userpick means a selection of the first and the second test elements by theuser 14. The audio, i.e. music picks presented to the users are analyzedand clusterized in the same fashion. The functionality of the algorithmis based on existence of correlation between user visual, i.e. graphicalillustration and audio preferences. The most significant correlationsare determined through reduction in distance between a particular userlocation and locations of the test elements to determine theaforementioned logical priority chain in order to determine classes ofthe users and store the same in the designated or pre-determinedcluster.

An advantage of the present invention is to provide the improved systemand method whereby users will enjoy playlist of songs based on theuser's choice and preference will be presented to the users based oninitial questionnaire.

Another advantage of the present invention is a novel system and methodwherein the users will not select songs to create the playlist therebyeliminating the need for creation of such playlist that istime-consuming and burdensome.

BRIEF DESCRIPTION OF THE DRAWINGS

Other advantages of the present invention will be readily appreciated asthe same becomes better understood by reference to the followingdetailed description when considered in connection with the accompanyingdrawings wherein:

FIG. 1 illustrates a schematic view of a system architecture of thepresent invention;

FIG. 2 illustrates a multidimensional surface defined by axis x and y ofthe inventive algorithm illustrating a location of a user (U1) andmultiple test elements (P1, P2, and P3) and distances D1, D2, and D3defined between U1 and P1, P2, and P3;

FIG. 3 illustrates the multidimensional surface defined by axis x and yof the FIG. 2 illustrating locations of the user (U1) and another user(U2) and multiple test elements P1, P2, and P3 and distances D1, D2, andD3 defined between U1 and U2 and P1, P2, and P3 thereby illustratingvarious distal relationships between U1 and U2 and P1, P2, and P3thereby identifying a pair of clusters that U1 and U2 belong to;

FIG. 4 illustrates the multidimensional surface defined by axis x and yof the FIG. 2 illustrating locations of a new or third user (U3) andmultiple test elements P1, P2, and P3 and distances D1, D2, and D3defined between U3 and P1, P2, and P3 thereby illustrating variousdistal relationships between U1, U2, and U3 and P1, P2, and P3 therebyidentifying what cluster U3 belongs to; and

FIGS. 5A and 5B illustrate a pair multidimensional surfaces such as afirst multidimensional surface of first test elements and a secondmultidimensional surface of second test elements thereby illustratinglocations of several users U1, U2, and U3 and its relationship torespective test elements P1, P2, and P3 and distances D1, D2, and D3defined between U1, U2, and U3 and P1, P2, and P3 thereby determiningcorrelation between the users preference in music and graphicalillustrations.

DESCRIPTION OF THE INVENTION

A system of the present invention is generally shown at 10 in FIG. 1.The system 10 present a web based application. The system 10 includes awebsite having at least an interface component 12 adaptable to receiveinformation from users 14 through the user personal devices such as, amobile phone, a personal computer, and the like. The number of the users14 is unlimited and reference to a first user and a second user as willbe mentioned below is not intended to limit the scope of the presentinvention and are presented for explanatory purposes. The interfacecomponent 12 is operably communicated with an image controller 16 forstoring and circulating first test elements, such as images, video, andother type of information to be presented to the users 14 so the userscan make a selection of the first test element based on the user'spreference. The system includes a music controller 18 for storing andcirculating second test elements, such as music, songs, and any othertypes of audio recording to be listened by the users 14 so the users 14can make a selection of the second test element based on the user'spreference. Each user 14 will use a screen that will appear of theuser's personal devices such as, the mobile phone, the personalcomputer, and the like. The screen (not shown) will provide the user 14with several options that will allow the user 14 to either indicate whatoption the user 14 likes or dislikes as the first and second testelements presented to be selected by the user or if the user prefers notto make a selection. Various screen designs may be used with the presentinvention without limiting the scope of the present invention. Thesystem 10 includes a coordinating controller 20 with all of theaforementioned components being cooperable with one another andadaptable to receive and exchange information between one and the other.

A central engine or a central processing unit (the CPU) 22 is adaptableto receive information from the coordinating controller 20. The CPU 22is communicated with a content delivery network or content distributionnetwork (CDN) 26. As appreciated by those skilled in the art the CDN 26is a system of computers containing copies of data placed at variousnodes, such as the nodes of the present invention. The CDN 26 improvesaccess to the data it caches by increasing access bandwidth andredundancy and reducing access latency. There numerous data types cachedin the CDN 26 of the present invention. These data includes web objects,downloadable objects (media files, software, documents), applications,live streaming media, and database queries without limiting the scope ofthe present invention.

The CPU 22 includes at least one sub component 24 with a softwarepresenting an algorithm. The software presents an operable connectionwith the coordinating component 20. The software of the CPU 22 isconfigured to map location of each user 14 on a multidimensional surfacepresented by an axis x and an axis y as best illustrated in FIG. 2.Those skilled in algorithmic art will appreciate that other dimensionalparameters may be used in connection with the present invention topresent a map to determine location of the users 14 relative to oneanother and the first and second test elements presented at FIGS. 2through 5 at P1 through P3. As shown in FIG. 2, the software isconfigured to algorithmically calculate various distances betweenlocations a first user U1 and the test elements P1 through P3, whereinvarious distances are presented by D1 through D3 based on preferences ofthe user D1 in relationship to each test element P1 through P3. Thesoftware will determine that the user U1 prefers based on each user's 14personal preferences the test element P1 the most and then in itsrespective consecutive order the test element 2 and then the testelement P3. The first test elements and the second test elements aredefined and are not limited to various forms of art such as graphicalillustrations, pictures, videos, songs, various melodies, and otherforms of art.

Referring to FIG. 3, a second user U2 is mapped on the multidimensionalsurface. Here, the same or similar test elements P1 through P4 arepresented to the second user U2. As illustrated in FIG. 3, the distancesbetween the users U1 and U2 and the test elements P1 through P4 aredifferent, which means that each of the users U1 and U2 have differentpreferences and prefer different test elements as their first, secondand third choices. For example, as shown in FIG. 3, the first user U1prefers the test element P1 as his first choice, and then the testelements P2, P3, and P4 consecutively.

Unlike the user U1, the second user U2 has different preference to thetest elements P1 through P4. The second user U2 prefers the test elementP3 as his first choice, and then the test elements P4, P2, and P1consecutively. Each plurality of the first test elements and the secondtest elements presented to and selected by the first and second users U1and U2 allow the software to determine consecutive orders of the firsttest elements and the second test elements relative to the locations ofeach of the first and second users U1 and U2 thereby identifying a firstcluster and a second cluster based on difference between values of theconsecutive orders.

Referring now to FIG. 4, a third user U3 is mapped on themultidimentional surface. Unlike the users U1 and U2, the third user U3has different preference to the test elements P1 through P4. The thirduser U3 prefers the test element P3 as his first choice, and then thetest elements P1, P4, and P2 consecutively. Here, the software willalgorithmically calculate various distances between the location of thethird user U3 and the plurality of the first test elements and thesecond test elements P1 through P4 presented to and selected by thethird user U3 to determine a consecutive order of the first testelements and the second test elements P1 through P4 relative to thelocation of the third user U3 thereby assigning the third user U3 eitherto the first cluster and the second cluster as the software determines amatch between the values of one of the consecutive orders of the firstand second users U1 and U2 and said value of the consecutive order ofthe third user U3.

In an example illustrated in FIG. 4, all three users U1, U2, and U3 willbelong to different clusters based on the preferences in their choicesof the test elements P1 through P4, i.e. a “logical priority chains” anddifferences between the distances between location of each user U1, U2,and U3 and locations of the test elements P1 through P4. The algorithmpicks various images of good dispersion, good average user rating fromthe initial set of images stored in the image controller 16, andpreviously viewed by many testers. The algorithm is not limited to justseveral images to be presented to each user. Numerous images may bepresented to the users. For example, if in a first combination of thefirst test lements, such as images or a first triplet, i.e. 1, 2, 3, theuser U1 selects the image 1, and in a second combination of the imagesor a second triplet 1, 2, 4, the user selects image 1, and then in athird combination of the images or a third triplet 2, 3, 4, the userselects image 4, then the chain of the images selected by the user U1will be presented as follows: 1>4>(2 and 3). Such chains are beingrestored in the CPU 22 for numerous combinations of images.

Then the CPU 22 will find the approximate coordinates of the picks ofeach user 14 in a space of properties in order to clusterize picks andthe users 14. The second test elements such audio, i.e. music picks alsopresent to the users U1 through U5 presented to the users are analyzedand clusterized in the same fashion as described above. As illustratedin FIGS. 5A and 5B, a first dimensional surface, generally shown at A,represents location of the users U1 through U5 in relationship to thefirst test elements P1 through P4, wherein the first test elements aregraphic images presented to each of the users U1 through U5. FIG. 5 alsoillustrates a second dimensional surface, generally shown at B,represents location of the users U1 through U5 in relationship to thesecond test elements P1 through P4, wherein the second test elements areaudio, i.e. music presented to each of the users U1 through U5.

The functionality of the algorithm is based on existence of correlationbetween the user visual, i.e. graphical illustration and the user audiopreferences. The most significant correlations are determined throughreduction in distance between a particular user location to determinethe aforementioned logical priority chain in order to determine classesof the users U1 through U5 and store the same in the designated orpre-determined cluster.

For example, let's assume that the first user U1 is presented number ofsets of images P1 through P3 with at least three images in each set ortriplet. The user U1 will pick one of the images that the users enjoysthe most. Let's also assume that the user U1 picked image 1 out of thefirst set of images. If we apply a formula based on likeness (L), theequation will be presented as follows: L (P1)>L (P2) & L (P1)>L (P3),which means that the user U1 likes the image 1 more than the image 2 andthe image 3. If we apply a formula based on difference or distance (D),then the equation will look as follows: D (P1)<D (P2) & D (P1)<D (P3),wherein the image 1 is closed to the user as compared to the differencebetween the image 2 and the image 3.

As the information received from the user U1 is applied to themultidimensional space as illustrated for example in FIG. 2, defined bythe vertical and horizontal axis x and y, each pick or test element P1through P3 and the user U1 is characterized by M-dimensional positionvector (x, y, . . . , m). Thus, if there are several users and each ofthe users has multiple picks and each of the users have differentpreferences as compared to the images 1, 2, and 3.

The software of the present invention presents numerous formulas forcalculations. As best shown in FIG. 2, the multidimensional space M isdefined by the horizontal axis x and the vertical axis y, the user U1was presented several choices of images or music defined by P1, P2, andP3. The user U1 prefers P1 to P2 and then to P3. The distance between U1and P1 is shorter than between U1 and P2. Same for U1-P1 and U1-P3. Eachuser's answer “I choose picture 1 from this 3 images” creates 2constraint equations. The set of constraints, based on user picks,defines the logic equations for distance correspondence:

D(U 1, P 1) < D(U 1, P 2) … D(Uu, Pv) < D(Uu, Pw)

After expansion and projecting to coordinate space, the following set ofequations, containing distance between user and picture calculationformula on both sides is as follows:

${\left. {{{\left. 1 \right)\mspace{14mu}\sqrt{\left( {{U\; 1_{x}} - {P\; 1_{x}}} \right)^{2} + \left( {{U\; 1_{y}} - {P\; 1_{y}}} \right)^{2}}} < \sqrt{\left( {{U\; 1_{x}} - {P\; 2_{x}}} \right)^{2} + \left( {{U\; 1_{y}} - {P\; 2_{y}}} \right)^{2}}}\ldots u} \right)\mspace{14mu}\sqrt{\left( {{Uu}_{x} - {Pv}_{x}} \right)^{2} + \left( {{Uu}_{y} - {Pv}_{y}} \right)^{2}}} < \sqrt{\left( {{Uu}_{x} - {Pw}_{x}} \right)^{2} + \left( {{Uu}_{y} - {Pw}_{y}} \right)^{2}}$

Alluding to the formula shown above, U1 x is the x'th coordinate of1^(st) user point, P2 y—y'th coordinate of 2^(nd) picture or musicpoint, etc. In general—U—user points, P—picture or music points, andsubscript means coordinate index in M-dimensional space. Equations above(one in line) are the 1^(st), . . . , u'th Constraint(i) functions. Theset of constraint functions is defined by training set of user marks.The algorithm will then find such kind of placement for picks and usercoordinates (in this M-dimensional space) to violate the minimum ofconstraints. So, in general, the following vector needs to be found:{U1x,U1y, . . . ,U1m, . . . ,Unx,Uny, . . . ,Unm, . . . ;P1x, . . . P1m,. . . ,Pwx,Pwy, . . . ,Pwm}such that

$\min\limits_{Z}{\sum\limits_{i = 0}^{i = u}{{Violation}\left( {{Constraint}\left( {i,{F(Z)}} \right)} \right)}}$wherein a constraint is the function from above, taking arguments i(constraint number) and F(Z), where F is a combinatorial function,giving one placement of all objects U and P in M-dimensional space,taking parameter Z, which is the all possible combinations ofplacements. Violation(x) function gets equation and returns 1 if it isfalse. The sample placement for such system of constraints presented asfollows:D(U1,P1)<D(U1,P2)D(U1,P1)<D(U1,P3)D(U1,P2)<D(U1,P4)D(U2,P3)<D(U2,P2)D(U2,P3)<D(U2,P4)D(U2,P3)<D(U2,P1)

Sample of such placement satisfying all constraints is shown on FIG. 3.As mentioned above, here, there are two users U1 and U2. There are fourtest elements such as the images or music options P1, P2, P3, and P4presented to the users U1 and U2 to be picked. FIG. 3 clearlyillustrates that the difference between each user U1 and U2 and each ofthe options P1, P2, P3, and P4. It means that each user U1 and U2 hasdifferent preferences to each option of the images or music P1, P2, P3,and P4. So, the purpose of the first stage of the algorithm is to findsuch placements for the input combination of all users' choices. Thesame approach is used to determine preferences in the images and themusic. The algorithm gets two various placements with correspondingpoints {U1, . . . , Un}. Then the algorithm determines through use ofnon-linear mapping the difference between or correlation between onespace, i.e. the image space to another, i.e. the music space. The set ofpoints {U1, . . . , Un} is determined after stage 1 in the image space.

For example, as best illustrated in FIG. 4, the algorithm performed twooperations wherein each of three users—U1, U2, and U3 were presenteddifferent choices of picks P1 through P4. Based on the distance betweeneach of the picks P1 through P4 in relationship to each user U1, U2, andU3, the algorithm determined a space based on locations of each user U1,U2, U3, and U4 in relation to one and the other. As each user U1, U2, U3makes the choices of images and music played as the images are presentedand picked, the algorithm determines distance between each of the picksP1 through P4 in relationship to each user U1, U2, and U3.

As each new user 14 uses the system 10, the user's location will bemapped on the multidimensional surface. The algorithm will determinepreferences or picks such as P1 through P4 for both images and music.Based on determination of a consecutive order of the picks P1 through P4relative to the location of each new user, the algorithm will assigneach new user to the clusters predetermined by the software based onprior calculation as the software determines a match between the valuesof one of the consecutive orders of the previous users and the value ofthe consecutive order of such new user. The system has numerousindustrial application that require clusterization of users based on thepreferences of the users. For example, the system 10 may include adatabase of certain songs, videos, and other form of performing art thatmembers of the different clusters may prefer to listed. The types ofthese forms of performing art may constantly be renewed and added on bythe system and provided to the users to be listed and viewed. Forexample, if any new user is assigned by the software to a predeterminedcluster based on such user's picks of first and second test elements,that user may be presented different songs and videos and the like asother members of the same or substantially similar cluster will enjoy.

While the invention has been described with reference to an exemplaryembodiment, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications may be made to adapt a particular situationor material to the teachings of the invention without departing from theessential scope thereof. Therefore, it is intended that the inventionnot be limited to the particular embodiment disclosed as the best modecontemplated for carrying out this invention, but that the inventionwill include all embodiments falling within the scope of the appendedclaims.

The invention claimed is:
 1. A system for determining personalpreferences of users in various forms of art whereby the users accesssaid system through personal communication devices, said systemcomprising: a user interface component for receiving information fromthe personal communication devices; a controller device for storingfirst and second test elements; a central processing unit operablycommunicated with said controller device and said user interfacecomponent for receiving information from the users to determinecorrelation between said first and second test elements presented to theusers through said user interface component; a software of said centralprocessing unit configured to calculate various distances betweenlocations of a first user and a second user and each of a plurality ofsaid first test elements and said second test elements presented to andselected by the first user and the second user respectively to determineconsecutive orders of said first test elements and said second testelements relative to said locations of each of the first user and thesecond user thereby identifying preferences of the first user in oneform of art and preferences of the second user in another form of artwhereby said preferences of the first user and the second user aredetermined by said software based on difference between values of saidconsecutive orders; presenting at least one of said preferences of artof the first user and the second user to a third user as said softwarecalculates various distances between a location of the third user andsaid plurality of said first test elements and said second test elementspresented to and selected by the third user to determine a consecutiveorder of said first test elements and said second test elements relativeto said location of the third user thereby identifying preference of thethird user in either forms of art of the first user and the second useras identified by said software as said software determines a matchbetween said values of one of said consecutive orders of the first userand the second user and values of said consecutive order of the thirduser; wherein said controller device includes a first sub-controller forstoring and circulating through said user interface component said firsttest elements and a second sub-controller for storing and circulatingthrough said user interface component said second test elements; andwherein values of said consecutive orders represent distances betweenlocations of each user and locations of each test element.
 2. The systemas set forth in claim 1, wherein said first test elements are furtherdefined by a plurality of various images including at least one ofgraphical illustrations, videos.
 3. The system as set forth in claim 2,wherein said second test elements are further defined by a plurality ofvarious sounds including at least one of songs, melodies.
 4. The systemas set forth in claim 3, wherein said software configured to map saidlocations of the first, second and third users and said first and secondtest elements.
 5. The system as set forth in claim 1, wherein said userinterface component includes at least one of a desktop computer, alaptop computer, a mobile phone.
 6. The system as set forth in claim 3,wherein said software further defines each of said consecutive orders byD1, D2, D3 . . . Dn and defines the users by U1, U2, U3 . . . Un andsaid first and second test elements by P1, P2, P3 . . . Pn.
 7. Thesystem as set forth in claim 6, wherein said software includes a formulato receive a set of equations, containing distance between at least oneof the first user, the second user, and the third user and said firstand second test elements, said formula presented by:${\left. {{{\left. 1 \right)\mspace{14mu}\sqrt{\left( {{U\; 1_{x}} - {P\; 1_{x}}} \right)^{2} + \left( {{U\; 1_{y}} - {P\; 1_{y}}} \right)^{2}}} < \sqrt{\left( {{U\; 1_{x}} - {P\; 2_{x}}} \right)^{2} + \left( {{U\; 1_{y}} - {P\; 2_{y}}} \right)^{2}}}\ldots u} \right)\mspace{14mu}\sqrt{\left( {{Uu}_{x} - {Pv}_{x}} \right)^{2} + \left( {{Uu}_{y} - {Pv}_{y}} \right)^{2}}} < \sqrt{\left( {{Uu}_{x} - {Pw}_{x}} \right)^{2} + \left( {{Uu}_{y} - {Pw}_{y}} \right)^{2}}$wherein U1 x is the x'th coordinate of U1, P2 y is the y'th coordinateof P2.